






"""simple docstring"""
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMAEForPreTraining, ViTMAEModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor



class        UpperCamelCase__ :







    """simple docstring"""







    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Tuple=1_3				,    UpperCamelCase_ :	int=3_0				,    UpperCamelCase_ :	int=2				,    UpperCamelCase_ :	Tuple=3				,    UpperCamelCase_ :	List[Any]=True				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	List[str]=3_2				,    UpperCamelCase_ :	List[Any]=5				,    UpperCamelCase_ :	Optional[int]=4				,    UpperCamelCase_ :	str=3_7				,    UpperCamelCase_ :	Union[str, Any]="gelu"				,    UpperCamelCase_ :	Union[str, Any]=0.1				,    UpperCamelCase_ :	int=0.1				,    UpperCamelCase_ :	List[str]=1_0				,    UpperCamelCase_ :	Optional[Any]=0.02				,    UpperCamelCase_ :	int=3				,    UpperCamelCase_ :	Tuple=0.6				,    UpperCamelCase_ :	Optional[int]=None				,    ):





           '''simple docstring'''

           __magic_name__      				=    parent
           __magic_name__      				=    batch_size
           __magic_name__      				=    image_size
           __magic_name__      				=    patch_size
           __magic_name__      				=    num_channels
           __magic_name__      				=    is_training
           __magic_name__      				=    use_labels
           __magic_name__      				=    hidden_size
           __magic_name__      				=    num_hidden_layers
           __magic_name__      				=    num_attention_heads
           __magic_name__      				=    intermediate_size
           __magic_name__      				=    hidden_act
           __magic_name__      				=    hidden_dropout_prob
           __magic_name__      				=    attention_probs_dropout_prob
           __magic_name__      				=    type_sequence_label_size
           __magic_name__      				=    initializer_range
           __magic_name__      				=    mask_ratio
           __magic_name__      				=    scope

           # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
           # (we add 1 for the [CLS] token)
           __magic_name__      				=    (image_size // patch_size) ** 2
           __magic_name__      				=    int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

           __magic_name__      				=    None
           if self.use_labels:
                  __magic_name__      				=    ids_tensor([self.batch_size]				,    self.type_sequence_label_size )

           __magic_name__      				=    self.get_config()

           return config, pixel_values, labels







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           return ViTMAEConfig(
               image_size=self.image_size				,    patch_size=self.patch_size				,    num_channels=self.num_channels				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_size=self.intermediate_size				,    hidden_act=self.hidden_act				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    is_decoder=UpperCamelCase_				,    initializer_range=self.initializer_range				,    mask_ratio=self.mask_ratio				,    )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Any ):





           '''simple docstring'''

           __magic_name__      				=    ViTMAEModel(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_ )
           self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    ViTMAEForPreTraining(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_ )
           __magic_name__      				=    (self.image_size // self.patch_size) ** 2
           __magic_name__      				=    self.patch_size**2 * self.num_channels
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, num_patches, expected_num_channels) )

           # test greyscale images
           __magic_name__      				=    1
           __magic_name__      				=    ViTMAEForPreTraining(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
           __magic_name__      				=    model(UpperCamelCase_ )
           __magic_name__      				=    self.patch_size**2
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, num_patches, expected_num_channels) )







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.prepare_config_and_inputs()
           __magic_name__				,  __magic_name__				,  __magic_name__      				=    config_and_inputs
           __magic_name__      				=    {'pixel_values': pixel_values}
           return config, inputs_dict



@require_torch
class        UpperCamelCase__ (				a_							,	a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    __UpperCAmelCase           = {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    ViTMAEModelTester(self )
           __magic_name__      				=    ConfigTester(self				,    config_class=UpperCamelCase_				,    has_text_modality=UpperCamelCase_				,    hidden_size=3_7 )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.config_tester.run_common_tests()







    @unittest.skip(reason='ViTMAE does not use inputs_embeds' )
    def 				a__			(		self :	int ):





           '''simple docstring'''

           pass







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  self.assertIsInstance(model.get_input_embeddings()				,    (nn.Module) )
                  __magic_name__      				=    model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(UpperCamelCase_				,    nn.Linear ) )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  __magic_name__      				=    inspect.signature(model.forward )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  __magic_name__      				=    [*signature.parameters.keys()]

                  __magic_name__      				=    ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,    UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_ )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Any ):





           '''simple docstring'''

           np.random.seed(2 )

           __magic_name__      				=    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
           __magic_name__      				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
           __magic_name__      				=    torch.from_numpy(UpperCamelCase_ )

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           __magic_name__      				=    pt_noise

           super().check_pt_tf_models(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  model.to(UpperCamelCase_ )
                  model.eval()
                  # make random mask reproducible
                  torch.manual_seed(2 )
                  with torch.no_grad():
                         __magic_name__      				=    model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_ ) )

                  __magic_name__      				=    outputs[0].cpu().numpy()
                  __magic_name__      				=    0

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         model.save_pretrained(UpperCamelCase_ )
                         __magic_name__      				=    model_class.from_pretrained(UpperCamelCase_ )
                         model.to(UpperCamelCase_ )
                         # make random mask reproducible
                         torch.manual_seed(2 )
                         with torch.no_grad():
                                __magic_name__      				=    model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_ ) )

                         # Make sure we don't have nans
                         __magic_name__      				=    after_outputs[0].cpu().numpy()
                         __magic_name__      				=    0
                         __magic_name__      				=    np.amax(np.abs(out_a - out_a ) )
                         self.assertLessEqual(UpperCamelCase_				,    1e-5 )







    @unittest.skip(
        reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.' )
    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           pass







    @unittest.skip(
        reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.' )
    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           pass







    @unittest.skip(
        reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.' )
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           pass







    @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           pass







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           pass







    @slow
    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __magic_name__      				=    ViTMAEModel.from_pretrained(UpperCamelCase_ )
                  self.assertIsNotNone(UpperCamelCase_ )


def 							A						(				)    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       return image






@require_torch
@require_vision
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           np.random.seed(2 )

           __magic_name__      				=    ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' ).to(UpperCamelCase_ )

           __magic_name__      				=    self.default_image_processor
           __magic_name__      				=    prepare_img()
           __magic_name__      				=    image_processor(images=UpperCamelCase_				,    return_tensors='pt' ).to(UpperCamelCase_ )

           # prepare a noise vector that will be also used for testing the TF model
           # (this way we can ensure that the PT and TF models operate on the same inputs)
           __magic_name__      				=    ViTMAEConfig()
           __magic_name__      				=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
           __magic_name__      				=    np.random.uniform(size=(1, num_patches) )

           # forward pass
           with torch.no_grad():
                  __magic_name__      				=    model(**UpperCamelCase_				,    noise=torch.from_numpy(UpperCamelCase_ ).to(device=UpperCamelCase_ ) )

           # verify the logits
           __magic_name__      				=    torch.Size((1, 1_9_6, 7_6_8) )
           self.assertEqual(outputs.logits.shape				,    UpperCamelCase_ )

           __magic_name__      				=    torch.tensor(
               [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )

           self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,    expected_slice.to(UpperCamelCase_ )				,    atol=1e-4 ) )







"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"